In the track of digitalisation, activities, such as searching for information or purchasing a product, which were formerly carried out by visiting a certain establishment, are nowadays performed via a computer. For example, instead of visiting a library in order to search for information, a search engine on the internet is used. Similarly, instead of visiting a physical shop for purchasing a product, the purchase is made via an internet shop.
Every day, thousands, millions or even billions of activities are performed online via computer interfaces, such as internet pages presented in a browser. As a result, huge data sets which are indicative of these activities are generated. These data sets may be analysed, for instance for the purpose of spotting trends among the activities. The trends may then be used to predict future activities, i.e., activities which have not yet been carried out. This may of course be of interest from a commercial perspective. However, it is also of interest from a more technical point of view. For example, the predictions may be used for adapting computer interfaces to facilitate performance of future activities.
Returning to the example of search engines above, the predictions could for instance be used to sort the results of a search in a list. The result in the first position in the list should be the result that the user making the search is most likely to choose (in view of what users typically have chosen), the result in the second position in the list should be the result that is the second most likely to be chosen, etc. By presenting the list to the user, the user may easily find what he or she is looking for. Moreover, the user may easily perform his or her next activity, such as browsing to a relevant internet page by clicking a link displayed in connection to the results in the list.
A related example is an auto-complete functionality. As a user types in the beginning of a word or sentence in a browser, a list may be presented to the user with suggestions for how to complete the word or the sentence. Also this list may be a prediction based on an analysis of trends of words and sentences which were previously entered by users.
Yet another example is an internet shop in which a user may be presented with a list of bestselling products from a product selection. The list may be a list of search results in accordance to what was described above, or a list on a category page or any other landing page, or a list of product recommendations on a product page.
Still another example is a list of articles from a digital newspaper or magazine. The list may give a ranking of articles based on which articles were read by readers of the newspaper or the magazine.
In all the above exemplified cases, the display of the list facilitates for a user to carry out a next activity, such as browsing to an internet page associated with an item on the list.
Lists according to the above are widely used on the internet. In order to serve the purpose of assisting a user in performing a next activity, it is important that the list provides an as accurate as possible prediction of the next activity. In other words, it is desirable that the next activity of a user is displayed in the list, and preferably as high up in the list as possible. There is therefore a need to provide a list which, based on past activities, as accurately as possible predicts future activities.
The lists that are used on the internet today are typically generated by a procedure which makes predictions based on trends of past activities with respect to a time horizon. For example, the procedure may work with a sliding time window, and rank the activities based on their occurrence within the sliding time window. With such procedures, the question of how to select the time horizon, i.e., the length of the sliding time window, arises. If a long time horizon is chosen, there is a risk that short but strong trends, so-called micro-trends, are missed. Conversely, if a short time horizon is chosen, long term trends will be missed. This will of course affect the accuracy of the predictions, i.e. the ranking, provided by the list. There is thus room for improvements.